Systems and methods for home value scoring

ABSTRACT

Systems and methods are provided for generating a model for providing one or more scores indicating a likelihood that an appraisal value is faulty. In one embodiment, a method includes receiving information representative of at least one of a borrower, a property, or one or more demographics; receiving a first appraisal value of the property; receiving a second appraisal value of the property, such that the second appraisal verifies the first appraisal; and determining one or more parameters for the model based on the received information, the received first appraisal value, and the received second appraisal value, such that the one or more parameters enable the model to provide the one or more scores.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 60/311,125, entitled “SYSTEMS AND METHODS FOR HOME VALUESCORING,” filed on Aug. 10, 2001, the disclosure of which is expresslyincorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

I. Field of the Invention

The present invention generally relates to financial systems and tosystems and methods for processing financial information. Moreparticularly, the invention relates to systems and methods forevaluating the likelihood that an appraisal of property is faulty.

II. Background and Material Information

When an application for a mortgage loan is processed by a financialentity such as a lender, bank, mortgage bank, mortgage broker, ormortgage originator, the property securing the mortgage is usuallyappraised. Since various financial entities have an interest in knowingthe true (or fair) market value of the property securing the mortgage,the appraisal of a property is an important part of the mortgage loanapplication process.

An appraisal provides a property value estimate indicating a marketvalue for a property. The appraisal may be performed in various waysincluding, for example, an in-person property appraisal performed by anappraiser. During the in-person appraisal, the appraiser physicallyinspects the property. Without a physical inspection of the property,recent sales information for comparable properties may be used togenerate an appraisal.

Alternatively, an automated valuation model serves as a tool thatutilizes various factors (e.g., ZIP code, lot size, number of bedrooms,etc.) to appraise a property. Examples of automated valuation models maybe found in one or more of the following applications: U.S. patentapplication Ser. No. 08/730,289, filed Oct. 11, 1996, entitled “METHODFOR COMBINING HOUSE PRICE FORECASTS,” U.S. patent application Ser. No.09/115,831, filed Jul. 15, 1998, entitled “SYSTEM AND METHOD FORPROVIDING HOUSE PRICE FORECASTS BASED ON REPEAT SALES MODEL,” U.S.patent application Ser. No. 09/134,161, filed Aug. 14, 1998, entitled“SYSTEM AND METHOD FOR PROVIDING PROPERTY VALUE ESTIMATES,” U.S. patentapplication Ser. No. 09/728,061, filed Dec. 4, 2000, entitled “METHODFOR FORECASTING HOUSE PRICES USING A DYNAMIC ERROR CORRECTION MODEL.”Other types of appraisals that provide an informed estimate of propertyvalue may also be used to appraise a property. Because of the variousforms that an appraisal may take, an appraisal may be burdensome for afinancial entity to process and/or interpret. For example, a financialentity may find it difficult to readily assess the reliability of anappraisal and, as a result, order an unnecessary reappraisal of theproperty.

A financial entity may use an appraisal to approve a mortgage loan. Forexample, when a borrower applies for a mortgage loan, the appraisal maybe used by a bank to verify the value of the underlying property. Thebank uses the property value as a factor in approving or rejecting themortgage loan application. For example, when an appraisal indicates thata property is worth less than the mortgage amount, a bank may not bewilling to accept the financial risk and will therefore reject themortgage loan application. On the other hand, the bank may simply tellthe borrower that the maximum amount borrowed cannot exceed theappraised property value, or a percentage thereof. For these and otherreasons, the appraisal is usually considered an important part of themortgage loan application process.

SUMMARY OF THE INVENTION

Accordingly, the present invention is directed to systems and methodsfor processing financial information and, more particularly, systems andmethods for evaluating the likelihood that an appraisal of property isfaulty.

A financial system consistent with the systems and methods of thepresent invention may provide an indication that an appraisal value fora property is likely to be faulty using a model including, for example,receiving information representative of at least one of a borrower, aproperty, or one or more demographics; receiving the appraisal value ofthe property; and determining a score based on the received information,received appraisal, and the model, such that the score provides theindication of the likelihood that the appraisal value for the propertyis faulty.

Additional features and advantages of the invention will be set forth inpart in the description which follows and in part will be obvious fromthe description, or may be learned by practice of the invention. Theobjectives and advantages of the invention may be realized and attainedby the system and method particularly described in the writtendescription and claims hereof as well as the appended drawings.

To achieve these and other advantages and in accordance with the purposeof the invention, as embodied and broadly described herein, there isalso provided a method for generating a model for providing one or morescores indicating a likelihood that an appraisal value is faulty,including, for example, receiving information representative of at leastone of a borrower, a property, or one or more demographics; receiving afirst appraisal value of the property; receiving a second appraisalvalue of the property, such that the second appraisal verifies the firstappraisal; and determining one or more parameters for the model based onthe received information, the received first appraisal value, and thereceived second appraisal value, such that the one or more parametersenable the model to provide the one or more scores.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as described. Further featuresand/or variations may be provided in addition to those set forth herein.For example, the present invention may be directed to variouscombinations and subcombinations of the disclosed features and/orcombinations and subcombinations of several further features disclosedbelow in the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various embodiments and aspectsof the present invention and, together with the description, explain theprinciples of the invention. In the drawings:

FIG. 1 illustrates an exemplary system environment in accordance withsystems and methods consistent with the present invention;

FIG. 2 is an exemplary block diagram for providing an indication that anappraisal value for a property is likely to be faulty using a modelconsistent with the systems and methods of the present invention;

FIG. 3A is an exemplary flowchart for providing a home value score basedon a model consistent with the systems and methods of the presentinvention;

FIG. 3B is an exemplary flowchart for generating a model consistent withthe systems and methods of the present invention;

FIG. 4 illustrates another exemplary system environment in accordancewith systems and methods consistent with the present invention;

FIG. 5 is another exemplary flowchart for providing a home value scoreconsistent with the systems and methods of the present invention;

FIG. 6 illustrates exemplary information received from a lenderconsistent with the systems and methods of the present invention;

FIG. 7 illustrates additional information that may be receivedconsistent with the systems and methods of the present invention;

FIG. 8 depicts an exemplary web page interface for providing informationconsistent with the systems and methods of the present invention;

FIG. 9 shows an exemplary model for determining an indication that anappraisal value for a property is likely to be faulty consistent withthe systems and methods of the present invention;

FIG. 10 depicts an exemplary web page interface for receiving anindication that an appraisal value for a property is likely to be faultyconsistent with the systems and methods of the present invention;

FIG. 11 is another exemplary flowchart for generating a model consistentwith the systems and methods of the present invention;

FIG. 12 is an exemplary flowchart for determining the one or moreparameters (or coefficients) of the model consistent with the systemsand methods of the present invention; and

FIG. 13 shows an exemplary table of information for determining the oneor more parameters of the model consistent with the systems and methodsof the present invention.

DETAILED DESCRIPTION

Reference will now be made in detail to the invention, examples of whichare illustrated in the accompanying drawings. Wherever possible, thesame reference numbers will be used throughout the drawings to refer tothe same or like parts.

Systems and methods consistent with the present invention permit afinancial entity, using a computing platform (or computer), to determinean indication of whether a property appraisal is likely to be faulty.Moreover, the financial entity may determine such indication in the formof a score, which is referred to herein as a Home Value (HV) Score. Inone aspect of the invention, the computing platform determines the HVScore based on a model and scales the score into a range (e.g., 300 to900) with a low score indicating a high likelihood that the appraisal isfaulty (i.e., the appraisal is probably “bad”) and a high scoreindicating a low likelihood that an appraisal is faulty (i.e., theappraisal is probably “good”).

By way of example only, a financial entity, such as a lender, bank,mortgage bank, mortgage broker, or mortgage originator, may process aborrower's mortgage loan application. That application may require anappraisal that is used to approve the mortgage loan application. Forexample, a mortgage originator may use the appraisal to determine aratio of the loan amount to the property value (referred to as aloan-to-value ratio or LTV) with the appraisal serving as the propertyvalue. Based on the loan-to-value ratio, the mortgage originator mayapprove the mortgage loan. However, if the appraisal is faulty, themortgage originator may then incorrectly approve the mortgage loanapplication. The mortgage originator thus has an interest in ensuringthat if the appraisal is faulty, the appraisal is discounted and a “new”appraisal is performed. Accordingly, by using the HV Score, a financialentity may readily assess the reliability of a property appraisalreducing the burden associated with processing and/or interpreting theappraisal. Moreover, because the HV Score makes it easier to interpretthe reliability of the appraisal, the financial entity will be lesslikely to misinterpret an appraisal and/or request an unnecessaryreappraisal.

In addition to evaluating an appraisal associated with an individualmortgage loan secured by real property, the HV Score may be used toevaluate each appraisal in a pool of mortgage loans. By way of exampleonly, quality control (QC) of the mortgage pool may be more accuratelytargeted to those mortgage loans secured by properties whose appraisalshave lower HV Scores. Rather than randomly selecting mortgage loans inthe pool for QC verification of an acceptable loan-to-value ratio, afinancial entity may use the HV Score to identify only those loans inthe pool most likely to have an unacceptable ratio. Accordingly, thefinancial entity may initiate further investigation (e.g., order a “new”appraisal) only of the mortgage loans identified based on thecorresponding HV Scores, reducing the financial and administrativeburden on the financial entity.

FIG. 1 shows an exemplary system 1000 for providing an indication ofwhether an appraisal is likely to be faulty, such that the indicationprovides an HV Score, enabling a financial entity to readily determinethe likelihood that the appraisal is likely to be faulty.

Referring to FIG. 1, the system includes a communication channel 1400,one or more lenders 1500,1510, one or more borrowers 1610, 1620, abroker 1700, one or more information sources 1800, and a processor 1350.The one or more lenders may include a financial entity, such as a bank,mortgage bank, mortgage broker, mortgage originator, and/or any otherentity seeking an indication of whether an appraisal value of a propertyis likely to be faulty. The one or more borrowers 1610, 1620 may includean entity, such as a consumer, seeking a mortgage loan. The broker 1700may include an entity that acts an agent, such as a mortgage broker. Theinformation source 1800 may include internal, external, proprietary,and/or public databases, such as financial databases and demographicdatabases. For example, sources of information may include DataQuickInformation Systems, International Data Management Inc., First AmericanCorporation, county property and/or tax records, TransUnion LLC, EquifaxInc., Experian, Department of Commerce, and Bureau of Labor andStatistics. The processor 1350 may include an entity capable ofprocessing information such that an HV Score is provided to, forexample, a lender 1500, borrower 1610, broker 1700 and/or any otherentity requesting an HV Score.

Although the communication channel 1400 is depicted in FIG. 1 asbi-directional, a skilled artisan would recognize that unidirectionalcommunication links may be used instead.

FIG. 2 depicts a functional block diagram associated with providing anHV Score consistent with the systems and methods of the presentinvention. Referring to FIGS. 1 and 2, an entity, such as a lender 1500providing a loan secured by a property, may provide the processor 1350with information, such as information describing the borrower 205,information describing the property 210, and/or information describingthe value of the property (e.g., an appraisal) 215. The processor 1350may then use a model 220 to determine an HV Score. The processor 1350may also provide the lender 1500 with the HV Score via a communicationchannel 1400.

In one aspect of the invention, the HV Score is scaled such that a lowHV Score indicates that an appraisal, provided by the lender, is likelyto be faulty (i.e., the property appraisal does not reflect the fairmarket value). On the other hand, a high HV Score indicates that anappraisal is unlikely to be faulty (i.e., the property appraisalaccurately reflects the fair market value). Accordingly, the HV Scorefacilitates determining whether an appraisal for a property is likely tobe faulty. Thus, the lender may more accurately evaluate the borrower'smortgage loan application.

FIG. 3A is an exemplary flowchart depicting steps for providing an HVScore. Referring to FIGS. 1 and 3A, in one embodiment, the processor1350 may receive information (step 3100) through a communication channel1400 from a mortgage originator, such as a lender. The receivedinformation may include, for example, information describing a borrower,a property, and an appraisal. In one aspect of the invention, theinformation describing the borrower may include the borrower's name,address, and credit history. Moreover, the property informationdescribes the property and may include, for example, the address of theproperty securing the mortgage. Furthermore, the appraisal informationprovides an estimate of the property value and may include, for example,any indication of property value, such as a borrower's estimate of theproperty value, a lender's estimate of the property value, a purchaseprice, an in-person appraisal by an appraiser, and/or an automatedvaluation model appraisal (or estimate) of the property's value.

The processor 1350 may then determine an HV Score, using the informationreceived from the mortgage originator, based on a model (step 3200). Theprocessor 1350 may also provide the HV Score (step 3300) to the mortgageoriginator, such as lender 1500, through the communication channel 1400.The lender 1500 may then use the HV Score to determine whether theappraisal information for a property is likely to be faulty. If theappraisal is likely to be faulty, the lender may then decide to orderanother appraisal.

In one embodiment, the processor 1350 may scale an HV Score such thatthe score falls within a range, such as 300-900. Table 1 shows threeexemplary HV Scores with a likelihood that an appraisal is faulty and aproposed action for the lender 1500. For example, when the processor1350 provides an HV Score of 500 to the lender 1500, the HV Score mayindicate that a property appraisal is highly likely to be unreliable (orfaulty). With an HV Score of 500, the HV Score may lead a lender 1500 toconclude that a review of the appraisal is appropriate, such asrequesting another appraisal (e.g., an in-person appraisal by anotherappraiser).

When the processor 1350 provides an HV Score of 600 to the lender 1500,the HV Score of 600 may indicate that a property appraisal is somewhatless likely to be unreliable than a score of 500. In this case, thelender 1500 may conclude that another appraisal is appropriate. However,since the HV Score is on the borderline of being reliable, the lender1500 may simply request a less costly automated appraisal using anautomated valuation model (AVM), such as Home Value Explorer^(SM) (HVE).The lender 1500 may then review the output from the AVM (includingrecent comparable sales in the neighborhood) to determine thereasonableness of the appraisal.

When the processor 1350 provides an HV Score of 700 to the lender 1500,the HV Score of 700 may indicate that a property appraisal is likely tobe more reliable than the score of 600. In this case, the lender 1500may be confident that the property appraisal is reliable. Accordingly,the lender 1500 may conclude that a reappraisal is unnecessary.

Although Table 1 shows three HV Scores between 500 and 700, any otherrange of HV Scores may be used instead including, for example, a rangeof HV Scores from 1 to 10 or 300 to 900. Moreover, although Table 1shows a lower likelihood of a faulty appraisal at higher HV Scores, askilled artisan would recognize that a lower likelihood of a faultyappraisal may be represented with lower HV Scores instead.

TABLE 1 Exemplary HV Scores HV SCORE FOR A LIKELIHOOD OF PROPERTY FAULTYAPPRAISAL PROPOSED ACTION 500 High Order a review of the appraisal 600Medium Order an appraisal using an automated valuation model and/orreview comparable recent sales (if any) 700 Low Do nothing

FIG. 3B is an exemplary flowchart depicting steps for generating amodel, such as an HV Score model, capable of providing an HV Score.Referring to FIGS. 1 and 3B, in one embodiment, the processor 1350 maybegin by receiving information from various sources of information(e.g., information source 1800) to enable the processor 1350 to generatethe HV Score model (step 3500). The processor 1350 may then process thereceived information (step 3600) to determine the coefficients (alsoreferred to as weights) that make up the HV Score model. The processor1350 may then provide the HV Score model to one or more entities (e.g.,lenders 1500, 1510 and/or brokers 1700) to permit those entities todetermine the HV Scores for appraisals. Referring again to FIG. 2, theHV Score model may thus be used as a model 220 to determine an HV Score230. Although an HV Score model is described herein, a skilled artisanwould recognize that any type of model that provides a score may be usedinstead.

In one aspect of the invention, the processor 1350 may periodically(e.g., yearly, monthly, etc.) update the HV Score model by providing anupdated set of HV Score model coefficients (step 3800).

FIG. 4 illustrates another exemplary system 4000 environment consistentwith the systems and methods of the present invention. As illustrated inFIG. 4, the system environment 4000 includes a processor 1350, one ormore lenders 1500, 1510, one or more borrowers 1610, 1620, one or morebrokers 1700, one or more information sources 1800, and a communicationchannel 1400. The processor 1350 may also include an input module 4100,an output module 4200, a computing platform 4300, and one or moredatabases 4600.

In one embodiment consistent with FIG. 4, the computing platform 4300may include a data processor such as a PC, UNIX server, or mainframecomputer for performing various functions and operations. Computingplatform 4300 may be implemented, for example, by a general purposecomputer or data processor selectively activated or reconfigured by astored computer program, or may be a specially constructed computingplatform for carrying-out the features and operations disclosed herein.Moreover, computing platform 4300 may be implemented or provided with awide variety of components or systems including, for example, one ormore of the following: one or more central processing units, aco-processor, memory, registers, and other data processing devices andsubsystems.

Communication channel 1400 may include, alone or in any suitablecombination a telephony-based network, a local area network (LAN), awide area network (WAN), a dedicated intranet, the Internet, or awireless network. Further, any suitable combination of wired and/orwireless components and systems may be incorporated into communicationchannel 1400. Although the computing platform 4300 may connect to thelenders 1500, 1510 through the communication channel 1400, computingplatform 4300 may connect directly to the lenders 1500, 1510.

Computing platform 4300 also communicates with input module 4100 and/oroutput module 4200 using connections or communication links, asillustrated in FIG. 4. Alternatively, communication between computingplatform 4300 and input module 4100 or output module 4200 may beachieved using a network (not shown) similar to that described above forcommunication channel 1400. A skilled artisan would recognize thatcomputing platform 4300 may be located in the same location or at ageographical separate location from input module 4100 and/or outputmodule 4200 by using dedicated communication links or a network.

Input module 4100 may be implemented with a wide variety of devices toreceive and/or provide information. Referring to FIG. 4, input module4100 may include an input device 4110, a storage device 4120, and/or anetwork interface 4130. Input device 4110 may also include a keyboard, amouse, a disk drive, telephone, or any other suitable input device forreceiving and/or providing information to computing platform 4300.Although FIG. 4 only illustrates a single input module 4100, a pluralityof input modules 4100 may also be used.

Storage device 4120 may be implemented with a wide variety of systems,subsystems and/or devices for providing memory or storage including, forexample, one or more of the following: a read-only memory (ROM) device,a random access memory (RAM) device, a tape or disk drive, an opticalstorage device, a magnetic storage device, a redundant array ofinexpensive disks (RAID), and/or any other device capable of providingstorage and/or memory.

Network interface 4130 may exchange data between the communicationchannel 1400 and computing platform 4300 and may also exchange databetween the input module 4100 and the computing platform 4300. In oneaspect of the invention, network interface 4130 may permit a connectionto at least one or more of the following networks: an Ethernet network,an Internet protocol network, a telephone network, a radio network, acellular network, or any other network capable of being connected toinput module 4100.

Output module 4200 may include a display 4210, a printer 4220, and/or anetwork interface 4230. The output module 4200 may be used to provide,inter alia, an HV Score to lenders 1500, 1510, provide an HV Score modelto a computing platform 4300, and/or provide the HV Score model to anyentity (or processor) seeking to determine an HV Score. Further, theoutput from computing platform 4300 may be displayed or viewed throughdisplay 4210 (e.g., a cathode ray tube or liquid crystal display) and/orprinter device 4220. For example, the HV Score may be viewed on display4210 and/or printed on printer device 4220. Although FIG. 4 onlyillustrates a single output module 4200, a plurality of spatiallyseparated output modules 4200 may be used.

The printer device 4220 may provide output that includes informationthat summarizes multiple HV Scores. The summary information may includeaverage HV Scores, percentage of HV Scores that fall above or below athreshold score, and/or other tabular/graphical information forsummarizing multiple HV Scores. Moreover, multiple HV Scores and thecorresponding summary information may also be categorized based onstate, lender (or lender branch), appraiser, or other user definedcategories.

Network interface 4230 exchanges data between the output module 4200 andthe computing platform 4300 and/or between the computing platform 4300and the communication channel 1400. The network interface 4230 maypermit connection to at least one or more of the following networks: anEthernet network, and Internet protocol network, a telephone network, acellular network, a radio network, or any other network capable of beingconnected to output module 4200.

The database 4600 may store information including financial information,demographic information, real estate information, credit information,and other public and/or proprietary information that is kept within anentity or organization. For example, the database 4600 may storeinformation received from the information source 1800 such asinformation from DataQuick Information Systems, International DataManagement Inc., First American Corporation, county property and/or taxrecords, TransUnion LLC, Equifax Inc., Experian, Department of Commerce,and Bureau of Labor and Statistics. Although the database 4600 is shownin FIG. 4 as being located with the computing platform 4300, a skilledartisan would recognize that the database (or databases) may be locatedanywhere (or in multiple locations) and connected to the computingplatform via direct links or networks.

FIG. 5 shows another exemplary flowchart with steps for providing the HVScore. Referring to FIGS. 4 and 5, the computing platform 4300 begins(step 5005) when it receives information (step 5100). For example, thecomputing platform 4300 may receive information provided by the lenders1500, 1510 through the communication channel 1400. The computingplatform 4300 may also receive information from other sources, such asthe information source 1800 and/or database 4600 (step 5200); initializeone or more variables for use in the HV Score model (step 5300);determine the HV Score based on the HV Score model and receivedinformation (step 5400); and end when it provides the HV Score (steps5500-5600).

To receive information provided by the lender (step 5100), the computingplatform 4300 may receive from the lender, such as lender 1500,information representative of a borrower, the property, or demographicinformation. In one aspect of the invention, the computing platform 4300may receive information from the lender 1500 through the communicationchannel 1400. This received information may include the informationdepicted in FIG. 6.

Referring to FIG. 6, the received information from the lender 1500 mayinclude one or more of the following: a loan reference number; theidentity of a lender; a street address of a property; the city, state,and ZIP code of the property; a stated value of the property (e.g., anappraisal provided by the borrower or the lender); an amountcorresponding to the total amount borrowed or secured by the property;and information indicating whether the mortgage loan is secured by acondominium, town house, single family home, 2-4 unit dwelling, ormultifamily dwelling. Moreover, the information received from the lender1500 may include information indicating the mortgage loan type, such aswhether the mortgage loan is for the purchase of a property, a mortgagerefinancing, a mortgage refinancing with cash returned to the borrower(referred to as a “cash out” refinance), a home improvement loan, a debtconsolidation loan, or any other type of mortgage loan. Furthermore, thereceived information may include an indication of the borrower's creditworthiness, such as a credit history or credit score(s); a flagindicating a source of the borrower credit information (e.g., creditinformation provided by a lender or a source of credit information);and/or any other information describing the borrower, the property, ordemographics associated with the borrower or the property.

Moreover, the stated value of the property may correspond to anappraisal value of the property. As noted above, the appraisal value ofa property may be any type of property appraisal including, for example,an in-person property appraisal, a borrower's property value estimate, alender's property value estimate, a sales price for the property, a loanamount, an automated valuation model (AVM) appraisal (e.g., an appraisalprovided by HVE).

Referring again to FIG. 5, to receive information from other sources(step 5200), the computing platform 4300 may interface with, or beembedded in, one or more systems (not shown), that provide financialinformation, credit information, and/or real estate information, such assystems that are used to originate loans, provide appraisals (or valueproperty), and/or provide quality control tools for the mortgage loanprocess.

In one aspect of the invention, the computing platform 4300 may alsointerface with one or more sources of information (e.g., database 4600and/or the information source 1800). The sources of information mayprovide, inter alia, median home price information for a region,borrower credit information (e.g., credit reports or credit scores),property appraisal information for one or more properties, and/or anyother information that may be a factor in determining the accuracy orreliability of a property appraisal.

The sources of information may also provide the computing platform 4300with the information listed in FIG. 7. Referring to FIG. 7, theinformation received by the computing platform 4300 may include one ormore of the following: a borrower's credit score(s); a ZIP code with itsplus 4 extension (if available) for a property; an AVM estimate, astandard deviation for the AVM estimate; and/or a value corresponding toa median price for a property in a region (listed in FIG. 7 as a “zonepoint value”). The region may correspond to a street, a neighborhood, acity, a ZIP code, a county, a census tract, a metropolitan statisticalarea, a state, and/or a country.

In one aspect of the invention, the information depicted in FIG. 6 isprovided via a web-based input. FIG. 8 shows a web page for providinginformation to a processor 1350 (or computing platform 4300) via thecommunication channel 1400 (e.g., the Internet). A lender, a borrower, abroker, and/or any other entity seeking an HV Score may access the webpage of FIG. 8 to provide processor 1350 (or computing platform 4300)with information. The information provided via the web page of FIG. 8may then be used by the computing platform 4300 to determine the HVScore.

Referring again to FIG. 5, to initialize variables for use in the HVScore model (step 5300), the computing platform 4300 may initialize oneor more of the variables in the HV Score model based on the informationreceived in steps 5100 and 5200. In one embodiment, the computingplatform 4300 may initialize the variables, such as the variables listedin Table 2 below.

Referring to Table 2, the computing platform 4300 may initialize one ormore variables based on the received information in steps 5100-5200. Forexample, the computing platform may initialize the variable “CS” with acredit score received from the lender. The variable “CS 660” and “CS760” adjusts the sensitivity of the HV Score when the variable “CS” isabove 660 or 760, respectively. The variable “MCRED” may provide a flagindicating that the credit score is missing. The variable “LTV”(loan-to-value) may correspond to the ratio of the loan amount to thefair market value of the property multiplied by 100. For example, amortgage of $100,000 on a property valued at $200,000 would have an“LTV” of 50. The variables “LTV 71,” LTV 81,” and “LTV 91” adjust thesensitivity of the HV Score to the variable “LTV” when “LTV” exceeds 70,80, or 90, respectively.

TABLE 2 Initialized Variables CS = credit score expressed in integers,e.g. 715 CS660 = max (CS-660,0) CS760 = max (CS-760,0) MCRED = 1 ifCredit Score is missing, otherwise 0; LTV = total loan-to-value ratio,expressed as an integer, e.g. 80 So (total loan amt/stated value)*100LTV71 = max (LTV-70,0) LTV81 = max (LTV-80,0) LTV91 = max (LTV-90,0)CONDO = 1 if property is Condominium, else 0 PUR = 1 if purpose ispurchase, else 0 NCO = 1 if purpose is rate/term refinance, else 0 CO =1 if purpose is cash out refinance, else 0 HIL = 1 if purpose is homeimprovement loan, else 0 DC = 1 if purpose is debt consolidation, else 0OTH = 1 if purpose is other, else 0 VALSIG = (log(stated value) −log(CPV))/St dev. (Logs are natural logs) If VALSIG > 0 then VALSIGU =VALSIG. Else VALSIGU = 0 If VALSIG < 0 then VALSIGD = VALSIG. ElseVALSIGD = 0 ZONDIF = (log(stated value) − log(Zone Point Value)). IfZONDIF > 0 then ZONDIFU = ZONDIF. Else ZONDIFU = 0 If ZONDIF < 0 thenZONDIFD = ZONDIF. Else ZONDIFD = 0 If Hedonic Point Value > 10,000 andRepeat Sales Point Value > 10,000 then NOTBOTH = 0; ELSE NOTBOTH = 1;VSU2 = max(VALSIGU-2.0,0);

In addition, the computing platform 4300 may initialize variables basedon the purpose (or type) of mortgage loan. For example, the variable“CONDO” may correspond to a flag that is a “1” when the property type isa condominium. The variable “PUR” may indicate that the mortgage purposecorresponds to a mortgage only for a property purchase (e.g., withoutcash out). The variable “NCO” may correspond to a “1” when the mortgagepurpose corresponds to a rate/term refinancing. The variable “CO” may beset to a value of “1” when the mortgage purpose corresponds to a cashout refinance (i.e., cash returned to the borrower). The variable “HIL”may indicate that the mortgage purpose corresponds to a home improvementloan. The variable “DC” may indicate that the mortgage purpose includesa borrower's debt consolidation mortgage loan. The variable “OTH” mayindicate that the mortgage purpose is unknown or other than the onesmentioned above.

The variable “VALSIG” may be determined based on the following equation:VALSIG=(log(stated value)−log(CPV))/St dev  Equation 1

where CPV means combined point value; where “St dev” represents thestandard deviation associated with the property valuations provided byan automated valuation model (AVM), such as HVE. The variable VALSIGrepresents the number of standard deviations (or sigmas (σ)) between thestated value (or appraisal) of the property and the estimated valueprovided by the AVM. In this example, the AVM provides a propertyvaluation estimate that is just one of the numerous factors consideredwhen determining the HV Score.

The variables “VALSIGU” and “VALSIGD” may be determined based on thevalue of “VALSIG,” as shown in Table 2. The value of the variable“ZONDIF” may be determined based on the following equation:ZONDIF=(log(stated value)−log(Zone Point Value))  Equation 2

The values of variable “ZONDIFU” and “ZONDIFD” may be determined basedon the value of “ZONDIF,” as shown in Table 2. Further, the variable“NOTBOTH” may be set to a value of zero when two types of automatedvaluation models are used (e.g., using a valuation model based on repeatsales versus one based on hedonics), while “NOTBOTH” is set to a valueof one when only a single type of automated valuation model is used.

The variable “VSU2” may be determined based on the value of the variable“VALSIGU,” as shown in Table 2. The variable VSU2 adjusts thesensitivity of the HV Score with respect to the VALSIG value of Equation1.

To determine the HV Score (step 5400 of FIG. 5), the computing platform4300 may use the HV Score model to compute the HV Score. Referring againto FIG. 2A, the HV Score model, may produce an HV Score 230 based on oneor more of the following: borrower information 205, property information210, and an appraisal value 215. In one aspect of the invention, thecomputing platform may determine an HV Score by multiplying theinitialized one or more variables from step 5300 by one or morecorresponding coefficients (or weights) that are part of the HV Scoremodel 220. Moreover, the computing platform 4300 may scale the HV Scoreinto a predetermined range, such as the range of 300 to 900. Thecomputing platform 4300 may then provide the scaled score as the HVScore to the lender or other entity that requested the score (step5500).

FIG. 9 shows an exemplary model, such as an HV Score model. Referring toFIG. 9, the computing platform 4300 may determine the product of theinitialized variable and corresponding model coefficient (lines 1-21).For example, computing platform 4300 would determine the product of thecoefficient “−11.0280” and the initialized variable “LTV” by multiplyingthese two values (line 6). As illustrated in FIG. 9, the computingplatform 4300 then sums all of the determined products to produce an HVScore.

Moreover, in one aspect of the invention, an HV Score is scaled into arange of 300 to 900 such that an HV Score of 300 suggests that anappraisal value received from a lender may be faulty. On the other hand,an HV Score of 900 would indicate that the appraisal value is likely tobe reliable. FIG. 9 at lines 23-24 shows that an HV Score that is lessthan 300 is scaled to 300 and an HV Score that is more than 900 isscaled to 900.

FIG. 10 depicts an exemplary web page with an HV Score that is providedto an entity, such as lender 1500. The computing platform 4300 mayprovide the HV Score to the lender 1500 through the communicationchannel 1400. As illustrated in FIG. 10, the HV Score may provide thelender 1500 with an indication of whether the property appraisal islikely to be faulty. By way of example only, FIG. 10 depicts that an HVScore below 500 maybe considered at “highest risk” of being faulty,suggesting to the lender 1500 that a review of the appraisal may beappropriate. A review of the appraisal may include a second appraisal,such as an in-person appraisal or automated valuation model appraisal.An HV Score between 500-600 may be considered at “moderate risk” ofbeing faulty, suggesting to the lender 1500 that it conduct a review ofcomparable recent home sales and/or an automated valuation modelappraisal. When an HV Score is above 700, the appraisal is at “lowestrisk” of being faulty, suggesting to the lender 1500 that no furtherreview or verification of the appraisal is necessary.

In one embodiment, the computing platform 4300 may also generate themodel, such as the HV Score model. FIG. 11 shows a flow chart depictingthe steps associated with generating the HV Score model. The computingplatform 4300 may begin by receiving historical (or truth) information(step 11100); determining one or more coefficients (or weights) for theHV Score model based on the received historical information (step11200); and ends when it provides the HV Score model (steps11300-11400).

The computing platform 1300 may receive historical information for oneor more loans from sources of information, such as database 4600 or theinformation source 1800. The historical information may include borrowerinformation, demographic information, loan information, and/or propertyinformation. Moreover, the historical information may includeinformation that is considered reliable and, preferably, verified (e.g.,“truth” data).

In one aspect of the invention, the computing platform 4300 usesappraisal information that is reliable and verified. For example, afirst appraisal performed on a property may be verified by a secondappraisal, such as an in-person appraisal. If a second appraisalconfirms the validity of the first appraisal, the first appraisal thusserves as historical information that is reliable and verified. Althoughunreliable and unverified data may also be used, the quality of the HVScore model may be improved by using reliable and verified data.

In one aspect of the invention, the computing platform 4300 may alsoreceive from a source of information (e.g., database 4600 or informationsource 1800) one or more of the following information that may serve ashistorical information: borrower credit information (e.g., credithistory), a credit score, a credit card balance, a credit card limit,and a ratio of a credit card balance to a credit card limit; aborrower's mortgage loan size; a borrower's car loan size; a borrower'sdelinquencies, such as 30, 60, or 90-day delinquencies (e.g., past duepayments on debt); a median (or average) income for a region, such as astreet, a neighborhood, a city, a ZIP code, a county, a state, acountry, a census tract, and/or a metropolitan statistical area; anindication of whether the borrower is a first time home buyer; a typeassociated with the loan, such as whether the loan is for a purchase, arefinance, or a cash-out refinance; a loan-to-value ratio for borrower'smortgage loan; a borrower's current home value; an indication of whetherthe mortgage loan is secured by a condominium, a single family home, atown house, a 2-4 unit dwelling, a multifamily dwelling, or a home in aplanned community; a number indicating the quantity of wage earners inthe borrower's household; a number indicating the quantity ofresidential units on a property; and/or any other information that maycontribute to generating an HV Score model. Moreover, the computingplatform 4300 may receive the historical information for a plurality ofloans.

To determine the coefficients (step 11200), the computing platform 4300may process the historical information received in step 11100 based onstatistical techniques, such as a logistic regression. By usingstatistical techniques, the computing platform 4300 may determine thecorresponding coefficients (or weights) of the HV Score model. Referringagain to FIG. 9, the exemplary HV Score model lists coefficientsincluding the following: 696.7000, +1.1513, +0.7011, −1.4889, +816.3115,−11.0280, +1.4715, +1.1859, −4.2848, −53.3393, −34.6074, +34.6074,−13.7633, +108.19, +67.90, +0, +0, +0, +0, −79.06, and +114.55. Thecomputing platform 4300 thus uses a statistical technique to determineeach of these coefficients.

In one embodiment, the computing platform 4300 may use a statisticaltechnique referred to as logistic regression to determine thecoefficients. Logistic regression models may be used to examine howvarious factors influence a binary outcome. An event (or result) thathas two possible outcomes is a binary outcome (e.g., good/bad orfaulty/reliable). Logistic modeling is available with many statisticalsoftware packages. For example, the commercially available statisticalpackages offered by SAS Institute Inc. include logistic regressionmodeling tools.

FIG. 12 shows an exemplary flow chart with steps for using a logisticregression approach. The logistic regression approach permitsdetermining coefficients for the HV Score model based on historicalinformation corresponding to one or more loans. Referring to FIG. 12,the computing platform 4300 may verify the first appraisal (step 12100);determine the outcome for each loan based on the historical informationand the verification of the appraisal (step 12200); determine thelikelihood (or probability) associated with each outcome (step 12300);determine one or more coefficients (or weights) for the HV Score model(step 12400); and adjust the one or more parameters by scaling the oneor more coefficients (or the estimated log odds/probability) into arange (step 12500).

To verify the first appraisal (step 12100), the computing platform 4300may compare the first appraisal with a second appraisal. Based on thecomparison, the computing platform 4300 may verify whether the firstappraisal is valid. For example, if the second appraisal is lower thanthe first appraisal, the first appraisal may be considered invalid. Onthe other hand, a second appraisal that is equal to or greater than thefirst appraisal may be considered valid. This second appraisal may be anin-person-appraisal, an AVM appraisal (or estimate), a comparison withcomparable recent sales, and/or any other appraisal of the propertyvalue. FIG. 13 shows an exemplary table showing received historicalinformation for loans with a second appraisal (shown as a “verifiedappraisal”). In this example, the computing platform 4300 may request asecond appraisal for each of the mortgage loans listed in FIG. 13 byeither requesting an in-person appraisal or requesting an AVM appraisal.

To determine the outcome for each mortgage loan (step 12200), thecomputing platform 4300 may compare the second appraisal to the firstappraisal. If the second appraisal is lower than the first appraisal,the computing platform 4300 may set the outcome to a “1.” An outcome of“1” may suggest that the first appraisal is faulty. On the other hand,if the verified appraisal is equal to or greater than the firstappraisal, the computing platform 4300 may set the outcome to “0.” Anoutcome of “0” may suggest that the first appraisal is likely to betrue. Referring again to FIG. 13, the computing platform 4300 thusprocesses each loan to determine an outcome based on the first appraisaland the verified second appraisal, storing the information depicted inFIG. 13 in the database 4600.

FIG. 13 also depicts a loan number, a first appraisal value, a verifiedappraisal value, an outcome, a loan-to-value ratio, a P factor (seebelow), credit information (e.g., a credit score or history), acondominium flag, and a cash out refinance flag. A skilled artisan wouldrecognize that additional information may also be received by thecomputing platform 4300 to determine the coefficients of the HV Scoremodel including any other information that provides an indication of anappraisal being faulty. For example, the additional information maycorrespond to the variables listed in Table 2.

The loan-to-value shown in FIG. 13 is the ratio of the loan amount tothe fair market value of the property multiplied by 100. The P Factormay be determined based on the following equation:

$\begin{matrix}{{P\mspace{14mu}{Factor}} = \frac{{LOG}\mspace{14mu}\left( {{appraisal}\mspace{14mu}{{value}/{AVM}}\mspace{14mu}{value}} \right)}{{AVM}\mspace{14mu}{standard}\mspace{14mu}{deviation}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

where LOG represents the natural logarithm; the appraisal valuerepresents the first appraisal; the AVM value represents an appraisalvalue provided by an AVM; and the AVM standard deviation represents thestandard deviation of the appraisal values provided by the AVM, such asHVE.

To determine the likelihood for each of the possible outcomes (step11300), the computing platform 4300 may further process the historicalinformation, using a logistic regression, to determine the odds that anoutcome is possible. For example, the computing platform 4300 maydetermine the likelihood that an appraisal value is faulty given itsloan-to-value, first appraisal, verified appraisal, loan-to-value ratio,P-Factor, credit score, condominium flag, and cash out refinance flag.

In one embodiment, the computing platform 4300 uses the followingequation to determine the odds, or likelihood that an outcome, such as afaulty appraisal, is possible:Log(p/1−p))=a+b ₁(LTV)+b ₂(P Factor)+b ₃(Credit Score)+b ₄(Condo Flag)+b₅(Cash Out Refinance Flag)+ . . . b _(n)(n ^(th) Variable)  Equation 4

where Log(p/(1−p)) represents the log odds (also referred to as LOGIT)that the appraisal value is likely to be faulty; p represents theprobability of a loan having a “0” outcome (or a “1” outcome); a, b₁,b₂, . . . b_(n) represent the initial coefficients of the HV Scoremodel; and n represents the number of coefficients used in the HV Scoremodel, where b_(n) represents the n^(th) coefficient. Before thecomputing platform 4300 utilizes a logistic regression, the values of a,b₁, b₂, . . . b_(n), and p may be unknown.

In this example, the computing platform 1500 uses five coefficients(i.e., n=5) corresponding to the following five variables: LTV, Pfactor, credit score, condo flag, and cash out refinance flag. Althoughthis example uses five coefficients, a skilled artisan would recognizethat additional coefficients and corresponding variables may be usedinstead.

Although p is an unknown value at the start of the logistic regression,p may conform to the following equation:p=1/(1+e ^(τ))  Equation 5

where τ is the following:τ=a+b ₁*LTV+b ₂ *P Factor+b ₃*Credit Score+b ₄*Condo Flag+b ₅*Cash OutRefinance Flag+ . . . b _(n)*Other variable(s).  Equation 6

The computing platform 4300 may then determine an estimate of thecoefficients of the HV Score model (step 12400). That is, the computingplatform 4300 may solve for an estimate of a, b₁, b₂ . . . b_(n) usingequations 4-6.

Although the computing platform 4300 may utilize a logistic regressionapproach as described in this example, a skilled artisan would recognizethat any other approach may be used instead to determine thecoefficients, such as the Probit regression approach available from SASInstitute Inc., standard regression, neural networks, and any otherstatistical or quantitative approach that may provide coefficients basedon historical information (or “truth” data).

Referring again to FIG. 12, to adjust the one or more parameters (step12500), the computing platform 1500 may then scale the coefficients a,b₁, b₂, . . . b_(n). In one embodiment, the computing platform 4300 mayscale the coefficients by multiplying each coefficient by the followingequation:actual coefficient=initial coefficient*(60/ln(2))  Equation 7

where ln is a natural logarithm. By using equation 7, the computingplatform 1500 may scale the initial coefficients such that every 60 HVScore points doubles the odds that an appraisal is likely to be faulty.The scaled coefficients may be used as the actual coefficients used inthe HV Score model, such as the HV Score model illustrated in FIG. 9.Accordingly, the computing platform 4300 may determine one or morecoefficients for the HV Score model based on a logistic regressionapproach using historical (or “truth”) information. The computingplatform 4300 may then use the HV Score model to determine the HV Score.

The system 1000 may be embodied in various forms including, for example,a data processor, such as the computer that also includes a database.Moreover, the above-noted features and other aspects and principles ofthe present invention may be implemented in various environments. Suchenvironments and related applications may be specially constructed forperforming the various processes and operations of the invention or theymay include a general-purpose computer or computing platform selectivelyactivated or reconfigured by code to provide the necessaryfunctionality. The processes disclosed herein are not inherently relatedto any particular computer or other apparatus, and may be implemented bya suitable combination of hardware, software, and/or firmware. Forexample, various general-purpose machines may be used with programswritten in accordance with teachings of the invention, or it may be moreconvenient to construct a specialized apparatus or system to perform therequired methods and techniques.

Systems and methods consistent with the present invention also includecomputer readable media that include program instruction or code forperforming various computer-implemented operations based on the methodsand processes of the invention. The media and program instructions maybe those specially designed and constructed for the purposes of theinvention, or they may be of the kind well known and available to thosehaving skill in the computer software arts. Examples of programinstructions include for example machine code, such as produced by acompiler, and files containing a high level code that can be executed bythe computer using an interpreter.

Furthermore, although the embodiments above refer to processinginformation related to mortgage loans secured by improved real property,systems and methods consistent with the present invention may processinformation related to other types of loans or credit instruments,including those secured by property, such as automobiles and/or personalproperty. Moreover, although reference is made herein to using the HVScore to assess a residential property for a mortgage loan, in itsbroadest sense systems and methods consistent with the present inventionmay provide a score for any type of property including commercialproperty.

1. A method for providing one or more scores indicating a likelihoodthat an appraisal value is faulty, said method comprising the steps of:receiving information representative of at least one of a borrower, aproperty, or one or more demographics; receiving a first appraisal valueof the property from a first source; receiving a second appraisal valueof the property from a second source, such that the second appraisalverifies the first appraisal; determining one or more parameters basedon the received information, the received first appraisal value, and thereceived second appraisal value; and providing the one or more scoresbased on the one or more parameters.
 2. The method of claim 1, whereinsaid step of receiving information further comprises the step of:receiving information for a plurality of loans.
 3. The method of claim2, wherein said step of receiving information further comprises the stepof: receiving the first appraisal value for each of the plurality ofloans.
 4. The method of claim 3, wherein said step of receiving thesecond appraisal further comprises the step of: receiving the secondappraisal value for each of the plurality of loans.
 5. The method ofclaim 4, wherein said step of determining further comprises the step of:verifying the first appraisal value based on the second appraisal value.6. The method of claim 5, wherein said step of determining furthercomprises the step of: determining one or more outcomes for each of theplurality of loans based on the corresponding first appraisal value andthe second appraisal value.
 7. The method of claim 6, wherein said stepof determining further comprises the step of: determining a probabilityfor each of the one or more outcomes.
 8. The method of claim 6, whereinsaid step of determining the probability further comprises the step of:determining the one or more parameters based on the one or moreoutcomes.
 9. The method of claim 1, wherein said step of determining theone or more parameters further comprises the step of: determining theone or more parameters based on a statistical technique.
 10. The methodof claim 9, further comprising the step of: defining the statisticaltechnique as a logistic regression.
 11. The method of claim 1, whereinsaid step of determining the one or more parameters further comprisesthe step of: determining the one or more parameters based on a neuralnetwork.
 12. The method of claim 1, further comprising the step of:providing the parameters.
 13. The method of claim 12, wherein said stepof providing further comprises the step of: providing the parametersusing the Internet.
 14. The method of claim 1, further comprising thestep of: adjusting the one or more parameters, such that a low scorecorresponds to a high likelihood that the appraisal value is faulty anda high score corresponds to a low likelihood that the appraisal value isfaulty.
 15. The method of claim 1, further comprising the step of:adjusting the one or more parameters, such that the one or more scoresare within a range of about 300 to about
 900. 16. The method of claim 1,further comprising the step of: adjusting the one or more parameters,such that a high score corresponds to a high likelihood that theappraisal value is faulty and a low score corresponds to a lowlikelihood that the appraisal value is faulty.
 17. A system forproviding one or more scores indicating a likelihood that an appraisalvalue is faulty, said system comprising: means for receiving informationrepresentative of at least one of a borrower, a property, or one or moredemographics; means for receiving a first appraisal value of theproperty from a first source; means for receiving a second appraisalvalue of the property from a second source, such that the secondappraisal verifies the first appraisal; means for determining one ormore parameters based on the received information, the received firstappraisal value, and the received second appraisal value; means forproviding the one or more scores based on the one or more parameters.18. A system for providing one or more scores indicating a likelihoodthat an appraisal value is faulty, said system comprising: at least onememory comprising: code that receives information representative of atleast one of a borrower, a property, or one or more demographics, codethat receives a first appraisal value of the property from a firstsource, code that receives a second appraisal value of the property froma second source, such that the second appraisal verifies the firstappraisal, code that determines one or more parameters based on thereceived information, the received first appraisal value, and thereceived second appraisal value, and code that provides the one or morescores based on the one or more parameters; and at least one dataprocessor that executes said code.
 19. A computer program product for asystem for providing one or more scores indicating a likelihood that anappraisal value is faulty, the computer program product comprising code,said code comprising: code that receives information representative ofat least one of a borrower, a property, or one or more demographics;code that receives a first appraisal value of the property from a firstsource; code that receives a second appraisal value of the property froma second source, such that the second appraisal verifies the firstappraisal; code that determines one or more parameters based on thereceived information, the received first appraisal value, and thereceived second appraisal value; and code that provides the one or morescores based on the one or more parameters.